Overview

Dataset statistics

Number of variables16
Number of observations4277
Missing cells1417
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory534.8 KiB
Average record size in memory128.0 B

Variable types

Text3
Categorical4
Boolean2
Numeric7

Alerts

VIP is highly imbalanced (87.2%)Imbalance
HomePlanet has 87 (2.0%) missing valuesMissing
CryoSleep has 93 (2.2%) missing valuesMissing
Cabin has 100 (2.3%) missing valuesMissing
Destination has 92 (2.2%) missing valuesMissing
Age has 91 (2.1%) missing valuesMissing
VIP has 93 (2.2%) missing valuesMissing
RoomService has 82 (1.9%) missing valuesMissing
FoodCourt has 106 (2.5%) missing valuesMissing
ShoppingMall has 98 (2.3%) missing valuesMissing
Spa has 101 (2.4%) missing valuesMissing
VRDeck has 80 (1.9%) missing valuesMissing
Name has 94 (2.2%) missing valuesMissing
Cabin_deck has 100 (2.3%) missing valuesMissing
Cabin_num has 100 (2.3%) missing valuesMissing
Cabin_side has 100 (2.3%) missing valuesMissing
PassengerId has unique valuesUnique
Age has 82 (1.9%) zerosZeros
RoomService has 2726 (63.7%) zerosZeros
FoodCourt has 2690 (62.9%) zerosZeros
ShoppingMall has 2744 (64.2%) zerosZeros
Spa has 2611 (61.0%) zerosZeros
VRDeck has 2757 (64.5%) zerosZeros

Reproduction

Analysis started2024-04-22 16:13:07.677682
Analysis finished2024-04-22 16:14:52.972174
Duration1 minute and 45.29 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

PassengerId
Text

UNIQUE 

Distinct4277
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:53.288594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters29939
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4277 ?
Unique (%)100.0%

Sample

1st row0013_01
2nd row0018_01
3rd row0019_01
4th row0021_01
5th row0023_01
ValueCountFrequency (%)
0013_01 1
 
< 0.1%
0046_02 1
 
< 0.1%
0075_01 1
 
< 0.1%
0019_01 1
 
< 0.1%
0021_01 1
 
< 0.1%
0023_01 1
 
< 0.1%
0027_01 1
 
< 0.1%
0029_01 1
 
< 0.1%
0032_01 1
 
< 0.1%
0032_02 1
 
< 0.1%
Other values (4267) 4267
99.8%
2024-04-22T18:14:53.892570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5982
20.0%
1 4806
16.1%
_ 4277
14.3%
2 2434
8.1%
3 2072
 
6.9%
5 1829
 
6.1%
4 1808
 
6.0%
7 1786
 
6.0%
6 1777
 
5.9%
8 1755
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5982
20.0%
1 4806
16.1%
_ 4277
14.3%
2 2434
8.1%
3 2072
 
6.9%
5 1829
 
6.1%
4 1808
 
6.0%
7 1786
 
6.0%
6 1777
 
5.9%
8 1755
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5982
20.0%
1 4806
16.1%
_ 4277
14.3%
2 2434
8.1%
3 2072
 
6.9%
5 1829
 
6.1%
4 1808
 
6.0%
7 1786
 
6.0%
6 1777
 
5.9%
8 1755
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5982
20.0%
1 4806
16.1%
_ 4277
14.3%
2 2434
8.1%
3 2072
 
6.9%
5 1829
 
6.1%
4 1808
 
6.0%
7 1786
 
6.0%
6 1777
 
5.9%
8 1755
 
5.9%

HomePlanet
Categorical

MISSING 

Distinct3
Distinct (%)0.1%
Missing87
Missing (%)2.0%
Memory size33.5 KiB
Earth
2263 
Europa
1002 
Mars
925 

Length

Max length6
Median length5
Mean length5.0183771
Min length4

Characters and Unicode

Total characters21027
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEarth
2nd rowEarth
3rd rowEuropa
4th rowEuropa
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 2263
52.9%
Europa 1002
23.4%
Mars 925
21.6%
(Missing) 87
 
2.0%

Length

2024-04-22T18:14:54.141042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T18:14:54.312123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 2263
54.0%
europa 1002
23.9%
mars 925
22.1%

Most occurring characters

ValueCountFrequency (%)
a 4190
19.9%
r 4190
19.9%
E 3265
15.5%
t 2263
10.8%
h 2263
10.8%
u 1002
 
4.8%
o 1002
 
4.8%
p 1002
 
4.8%
M 925
 
4.4%
s 925
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4190
19.9%
r 4190
19.9%
E 3265
15.5%
t 2263
10.8%
h 2263
10.8%
u 1002
 
4.8%
o 1002
 
4.8%
p 1002
 
4.8%
M 925
 
4.4%
s 925
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4190
19.9%
r 4190
19.9%
E 3265
15.5%
t 2263
10.8%
h 2263
10.8%
u 1002
 
4.8%
o 1002
 
4.8%
p 1002
 
4.8%
M 925
 
4.4%
s 925
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4190
19.9%
r 4190
19.9%
E 3265
15.5%
t 2263
10.8%
h 2263
10.8%
u 1002
 
4.8%
o 1002
 
4.8%
p 1002
 
4.8%
M 925
 
4.4%
s 925
 
4.4%

CryoSleep
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing93
Missing (%)2.2%
Memory size33.5 KiB
False
2640 
True
1544 
(Missing)
 
93
ValueCountFrequency (%)
False 2640
61.7%
True 1544
36.1%
(Missing) 93
 
2.2%
2024-04-22T18:14:54.507473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Cabin
Text

MISSING 

Distinct3265
Distinct (%)78.2%
Missing100
Missing (%)2.3%
Memory size33.5 KiB
2024-04-22T18:14:54.877603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0813981
Min length5

Characters and Unicode

Total characters29579
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2714 ?
Unique (%)65.0%

Sample

1st rowG/3/S
2nd rowF/4/S
3rd rowC/0/S
4th rowC/1/S
5th rowF/5/S
ValueCountFrequency (%)
g/160/p 8
 
0.2%
g/748/s 7
 
0.2%
b/31/p 7
 
0.2%
e/228/s 7
 
0.2%
d/273/s 7
 
0.2%
c/31/s 6
 
0.1%
b/242/p 6
 
0.1%
c/295/p 6
 
0.1%
g/597/p 6
 
0.1%
g/737/s 6
 
0.1%
Other values (3255) 4111
98.4%
2024-04-22T18:14:55.550311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 8354
28.2%
1 2598
 
8.8%
S 2093
 
7.1%
P 2084
 
7.0%
2 1549
 
5.2%
F 1445
 
4.9%
4 1279
 
4.3%
3 1264
 
4.3%
G 1222
 
4.1%
5 1110
 
3.8%
Other values (11) 6581
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 8354
28.2%
1 2598
 
8.8%
S 2093
 
7.1%
P 2084
 
7.0%
2 1549
 
5.2%
F 1445
 
4.9%
4 1279
 
4.3%
3 1264
 
4.3%
G 1222
 
4.1%
5 1110
 
3.8%
Other values (11) 6581
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 8354
28.2%
1 2598
 
8.8%
S 2093
 
7.1%
P 2084
 
7.0%
2 1549
 
5.2%
F 1445
 
4.9%
4 1279
 
4.3%
3 1264
 
4.3%
G 1222
 
4.1%
5 1110
 
3.8%
Other values (11) 6581
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 8354
28.2%
1 2598
 
8.8%
S 2093
 
7.1%
P 2084
 
7.0%
2 1549
 
5.2%
F 1445
 
4.9%
4 1279
 
4.3%
3 1264
 
4.3%
G 1222
 
4.1%
5 1110
 
3.8%
Other values (11) 6581
22.2%

Destination
Categorical

MISSING 

Distinct3
Distinct (%)0.1%
Missing92
Missing (%)2.2%
Memory size33.5 KiB
TRAPPIST-1e
2956 
55 Cancri e
841 
PSO J318.5-22
388 

Length

Max length13
Median length11
Mean length11.185424
Min length11

Characters and Unicode

Total characters46811
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd row55 Cancri e
4th rowTRAPPIST-1e
5th rowTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 2956
69.1%
55 Cancri e 841
 
19.7%
PSO J318.5-22 388
 
9.1%
(Missing) 92
 
2.2%

Length

2024-04-22T18:14:55.800243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T18:14:55.978024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 2956
47.3%
55 841
 
13.4%
cancri 841
 
13.4%
e 841
 
13.4%
pso 388
 
6.2%
j318.5-22 388
 
6.2%

Most occurring characters

ValueCountFrequency (%)
P 6300
13.5%
T 5912
12.6%
e 3797
 
8.1%
S 3344
 
7.1%
- 3344
 
7.1%
1 3344
 
7.1%
A 2956
 
6.3%
I 2956
 
6.3%
R 2956
 
6.3%
5 2070
 
4.4%
Other values (13) 9832
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 6300
13.5%
T 5912
12.6%
e 3797
 
8.1%
S 3344
 
7.1%
- 3344
 
7.1%
1 3344
 
7.1%
A 2956
 
6.3%
I 2956
 
6.3%
R 2956
 
6.3%
5 2070
 
4.4%
Other values (13) 9832
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 6300
13.5%
T 5912
12.6%
e 3797
 
8.1%
S 3344
 
7.1%
- 3344
 
7.1%
1 3344
 
7.1%
A 2956
 
6.3%
I 2956
 
6.3%
R 2956
 
6.3%
5 2070
 
4.4%
Other values (13) 9832
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 6300
13.5%
T 5912
12.6%
e 3797
 
8.1%
S 3344
 
7.1%
- 3344
 
7.1%
1 3344
 
7.1%
A 2956
 
6.3%
I 2956
 
6.3%
R 2956
 
6.3%
5 2070
 
4.4%
Other values (13) 9832
21.0%

Age
Real number (ℝ)

MISSING  ZEROS 

Distinct79
Distinct (%)1.9%
Missing91
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean28.658146
Minimum0
Maximum79
Zeros82
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:56.201045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median26
Q337
95-th percentile55
Maximum79
Range79
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.179072
Coefficient of variation (CV)0.49476583
Kurtosis0.21852293
Mean28.658146
Median Absolute Deviation (MAD)8
Skewness0.48480029
Sum119963
Variance201.04607
MonotonicityNot monotonic
2024-04-22T18:14:56.459944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 176
 
4.1%
22 163
 
3.8%
19 162
 
3.8%
20 160
 
3.7%
24 158
 
3.7%
21 157
 
3.7%
25 156
 
3.6%
23 144
 
3.4%
26 132
 
3.1%
27 127
 
3.0%
Other values (69) 2651
62.0%
ValueCountFrequency (%)
0 82
1.9%
1 27
 
0.6%
2 35
0.8%
3 34
0.8%
4 20
 
0.5%
5 20
 
0.5%
6 25
 
0.6%
7 13
 
0.3%
8 24
 
0.6%
9 21
 
0.5%
ValueCountFrequency (%)
79 2
 
< 0.1%
78 1
 
< 0.1%
77 1
 
< 0.1%
75 2
 
< 0.1%
74 2
 
< 0.1%
73 5
0.1%
72 3
0.1%
71 2
 
< 0.1%
70 2
 
< 0.1%
69 6
0.1%

VIP
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing93
Missing (%)2.2%
Memory size33.5 KiB
False
4110 
True
 
74
(Missing)
 
93
ValueCountFrequency (%)
False 4110
96.1%
True 74
 
1.7%
(Missing) 93
 
2.2%
2024-04-22T18:14:56.738990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

MISSING  ZEROS 

Distinct842
Distinct (%)20.1%
Missing82
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean219.26627
Minimum0
Maximum11567
Zeros2726
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:56.930375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q353
95-th percentile1274.5
Maximum11567
Range11567
Interquartile range (IQR)53

Descriptive statistics

Standard deviation607.01129
Coefficient of variation (CV)2.7683751
Kurtosis53.216268
Mean219.26627
Median Absolute Deviation (MAD)0
Skewness5.5583897
Sum919822
Variance368462.7
MonotonicityNot monotonic
2024-04-22T18:14:57.216380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2726
63.7%
1 68
 
1.6%
2 34
 
0.8%
3 28
 
0.7%
4 24
 
0.6%
6 16
 
0.4%
5 15
 
0.4%
9 13
 
0.3%
8 12
 
0.3%
13 11
 
0.3%
Other values (832) 1248
29.2%
(Missing) 82
 
1.9%
ValueCountFrequency (%)
0 2726
63.7%
1 68
 
1.6%
2 34
 
0.8%
3 28
 
0.7%
4 24
 
0.6%
5 15
 
0.4%
6 16
 
0.4%
7 8
 
0.2%
8 12
 
0.3%
9 13
 
0.3%
ValueCountFrequency (%)
11567 1
< 0.1%
7407 1
< 0.1%
6438 1
< 0.1%
5900 1
< 0.1%
5862 1
< 0.1%
5454 1
< 0.1%
5333 1
< 0.1%
5100 1
< 0.1%
4922 1
< 0.1%
4908 1
< 0.1%

FoodCourt
Real number (ℝ)

MISSING  ZEROS 

Distinct902
Distinct (%)21.6%
Missing106
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean439.4843
Minimum0
Maximum25273
Zeros2690
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:57.488140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q378
95-th percentile2518.5
Maximum25273
Range25273
Interquartile range (IQR)78

Descriptive statistics

Standard deviation1527.663
Coefficient of variation (CV)3.4760356
Kurtosis67.764434
Mean439.4843
Median Absolute Deviation (MAD)0
Skewness6.9106254
Sum1833089
Variance2333754.4
MonotonicityNot monotonic
2024-04-22T18:14:57.713234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2690
62.9%
1 59
 
1.4%
2 30
 
0.7%
4 22
 
0.5%
3 21
 
0.5%
6 20
 
0.5%
5 19
 
0.4%
7 13
 
0.3%
11 12
 
0.3%
10 12
 
0.3%
Other values (892) 1273
29.8%
(Missing) 106
 
2.5%
ValueCountFrequency (%)
0 2690
62.9%
1 59
 
1.4%
2 30
 
0.7%
3 21
 
0.5%
4 22
 
0.5%
5 19
 
0.4%
6 20
 
0.5%
7 13
 
0.3%
8 11
 
0.3%
9 8
 
0.2%
ValueCountFrequency (%)
25273 1
< 0.1%
23397 1
< 0.1%
20809 1
< 0.1%
20229 1
< 0.1%
16963 1
< 0.1%
16954 1
< 0.1%
16250 1
< 0.1%
16071 1
< 0.1%
12350 1
< 0.1%
11984 1
< 0.1%

ShoppingMall
Real number (ℝ)

MISSING  ZEROS 

Distinct715
Distinct (%)17.1%
Missing98
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean177.29553
Minimum0
Maximum8292
Zeros2744
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:57.911912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333
95-th percentile994.1
Maximum8292
Range8292
Interquartile range (IQR)33

Descriptive statistics

Standard deviation560.82112
Coefficient of variation (CV)3.1631995
Kurtosis68.221142
Mean177.29553
Median Absolute Deviation (MAD)0
Skewness6.8249391
Sum740918
Variance314520.33
MonotonicityNot monotonic
2024-04-22T18:14:58.143236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2744
64.2%
1 72
 
1.7%
3 35
 
0.8%
2 32
 
0.7%
4 24
 
0.6%
7 19
 
0.4%
9 17
 
0.4%
8 16
 
0.4%
12 13
 
0.3%
10 12
 
0.3%
Other values (705) 1195
27.9%
(Missing) 98
 
2.3%
ValueCountFrequency (%)
0 2744
64.2%
1 72
 
1.7%
2 32
 
0.7%
3 35
 
0.8%
4 24
 
0.6%
5 11
 
0.3%
6 12
 
0.3%
7 19
 
0.4%
8 16
 
0.4%
9 17
 
0.4%
ValueCountFrequency (%)
8292 1
< 0.1%
8251 1
< 0.1%
8098 1
< 0.1%
8017 1
< 0.1%
7022 1
< 0.1%
6252 1
< 0.1%
6108 1
< 0.1%
6061 1
< 0.1%
6023 1
< 0.1%
5649 1
< 0.1%

Spa
Real number (ℝ)

MISSING  ZEROS 

Distinct833
Distinct (%)19.9%
Missing101
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean303.05244
Minimum0
Maximum19844
Zeros2611
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:58.392188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile1525
Maximum19844
Range19844
Interquartile range (IQR)50

Descriptive statistics

Standard deviation1117.186
Coefficient of variation (CV)3.6864445
Kurtosis80.460402
Mean303.05244
Median Absolute Deviation (MAD)0
Skewness7.6902979
Sum1265547
Variance1248104.6
MonotonicityNot monotonic
2024-04-22T18:14:58.616908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2611
61.0%
1 72
 
1.7%
2 43
 
1.0%
3 29
 
0.7%
4 27
 
0.6%
6 23
 
0.5%
8 22
 
0.5%
7 19
 
0.4%
5 16
 
0.4%
9 16
 
0.4%
Other values (823) 1298
30.3%
(Missing) 101
 
2.4%
ValueCountFrequency (%)
0 2611
61.0%
1 72
 
1.7%
2 43
 
1.0%
3 29
 
0.7%
4 27
 
0.6%
5 16
 
0.4%
6 23
 
0.5%
7 19
 
0.4%
8 22
 
0.5%
9 16
 
0.4%
ValueCountFrequency (%)
19844 1
< 0.1%
15733 1
< 0.1%
15255 1
< 0.1%
14252 1
< 0.1%
13983 1
< 0.1%
12842 1
< 0.1%
12767 1
< 0.1%
12690 1
< 0.1%
12437 1
< 0.1%
11483 1
< 0.1%

VRDeck
Real number (ℝ)

MISSING  ZEROS 

Distinct796
Distinct (%)19.0%
Missing80
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean310.71003
Minimum0
Maximum22272
Zeros2757
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:14:58.913783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q336
95-th percentile1536.8
Maximum22272
Range22272
Interquartile range (IQR)36

Descriptive statistics

Standard deviation1246.9947
Coefficient of variation (CV)4.0133714
Kurtosis93.842398
Mean310.71003
Median Absolute Deviation (MAD)0
Skewness8.38721
Sum1304050
Variance1554995.9
MonotonicityNot monotonic
2024-04-22T18:14:59.161613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2757
64.5%
1 72
 
1.7%
2 38
 
0.9%
3 33
 
0.8%
7 23
 
0.5%
6 21
 
0.5%
4 20
 
0.5%
5 17
 
0.4%
8 10
 
0.2%
19 10
 
0.2%
Other values (786) 1196
28.0%
(Missing) 80
 
1.9%
ValueCountFrequency (%)
0 2757
64.5%
1 72
 
1.7%
2 38
 
0.9%
3 33
 
0.8%
4 20
 
0.5%
5 17
 
0.4%
6 21
 
0.5%
7 23
 
0.5%
8 10
 
0.2%
9 9
 
0.2%
ValueCountFrequency (%)
22272 1
< 0.1%
19086 1
< 0.1%
18670 1
< 0.1%
16514 1
< 0.1%
15940 1
< 0.1%
15125 1
< 0.1%
14834 1
< 0.1%
14587 1
< 0.1%
14268 1
< 0.1%
12863 1
< 0.1%

Name
Text

MISSING 

Distinct4176
Distinct (%)99.8%
Missing94
Missing (%)2.2%
Memory size33.5 KiB
2024-04-22T18:14:59.547694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.756634
Min length7

Characters and Unicode

Total characters57544
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4169 ?
Unique (%)99.7%

Sample

1st rowNelly Carsoning
2nd rowLerome Peckers
3rd rowSabih Unhearfus
4th rowMeratz Caltilter
5th rowBrence Harperez
ValueCountFrequency (%)
extraly 14
 
0.2%
hopperett 13
 
0.2%
tranklinay 11
 
0.1%
apple 10
 
0.1%
garrez 10
 
0.1%
dickley 10
 
0.1%
petton 9
 
0.1%
logannon 9
 
0.1%
brie 9
 
0.1%
emenez 9
 
0.1%
Other values (3821) 8262
98.8%
2024-04-22T18:15:00.118693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6188
 
10.8%
a 5010
 
8.7%
n 4535
 
7.9%
4183
 
7.3%
r 3692
 
6.4%
o 3225
 
5.6%
l 3097
 
5.4%
i 3011
 
5.2%
s 2657
 
4.6%
t 2246
 
3.9%
Other values (43) 19700
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6188
 
10.8%
a 5010
 
8.7%
n 4535
 
7.9%
4183
 
7.3%
r 3692
 
6.4%
o 3225
 
5.6%
l 3097
 
5.4%
i 3011
 
5.2%
s 2657
 
4.6%
t 2246
 
3.9%
Other values (43) 19700
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6188
 
10.8%
a 5010
 
8.7%
n 4535
 
7.9%
4183
 
7.3%
r 3692
 
6.4%
o 3225
 
5.6%
l 3097
 
5.4%
i 3011
 
5.2%
s 2657
 
4.6%
t 2246
 
3.9%
Other values (43) 19700
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6188
 
10.8%
a 5010
 
8.7%
n 4535
 
7.9%
4183
 
7.3%
r 3692
 
6.4%
o 3225
 
5.6%
l 3097
 
5.4%
i 3011
 
5.2%
s 2657
 
4.6%
t 2246
 
3.9%
Other values (43) 19700
34.2%

Cabin_deck
Categorical

MISSING 

Distinct8
Distinct (%)0.2%
Missing100
Missing (%)2.3%
Memory size33.5 KiB
F
1445 
G
1222 
E
447 
B
362 
C
355 
Other values (3)
346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4177
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowF
3rd rowC
4th rowC
5th rowF

Common Values

ValueCountFrequency (%)
F 1445
33.8%
G 1222
28.6%
E 447
 
10.5%
B 362
 
8.5%
C 355
 
8.3%
D 242
 
5.7%
A 98
 
2.3%
T 6
 
0.1%
(Missing) 100
 
2.3%

Length

2024-04-22T18:15:00.381254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T18:15:00.604066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 1445
34.6%
g 1222
29.3%
e 447
 
10.7%
b 362
 
8.7%
c 355
 
8.5%
d 242
 
5.8%
a 98
 
2.3%
t 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 1445
34.6%
G 1222
29.3%
E 447
 
10.7%
B 362
 
8.7%
C 355
 
8.5%
D 242
 
5.8%
A 98
 
2.3%
T 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 1445
34.6%
G 1222
29.3%
E 447
 
10.7%
B 362
 
8.7%
C 355
 
8.5%
D 242
 
5.8%
A 98
 
2.3%
T 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 1445
34.6%
G 1222
29.3%
E 447
 
10.7%
B 362
 
8.7%
C 355
 
8.5%
D 242
 
5.8%
A 98
 
2.3%
T 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 1445
34.6%
G 1222
29.3%
E 447
 
10.7%
B 362
 
8.7%
C 355
 
8.5%
D 242
 
5.8%
A 98
 
2.3%
T 6
 
0.1%

Cabin_num
Real number (ℝ)

MISSING 

Distinct1505
Distinct (%)36.0%
Missing100
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean610.17884
Minimum0
Maximum1890
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-04-22T18:15:00.884362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q1174
median442
Q31027
95-th percentile1576.2
Maximum1890
Range1890
Interquartile range (IQR)853

Descriptive statistics

Standard deviation514.96813
Coefficient of variation (CV)0.84396262
Kurtosis-0.78044905
Mean610.17884
Median Absolute Deviation (MAD)341
Skewness0.68395888
Sum2548717
Variance265192.18
MonotonicityNot monotonic
2024-04-22T18:15:01.172194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 21
 
0.5%
31 18
 
0.4%
294 16
 
0.4%
197 16
 
0.4%
34 14
 
0.3%
228 14
 
0.3%
41 13
 
0.3%
231 13
 
0.3%
160 13
 
0.3%
184 13
 
0.3%
Other values (1495) 4026
94.1%
(Missing) 100
 
2.3%
ValueCountFrequency (%)
0 7
 
0.2%
1 5
 
0.1%
2 5
 
0.1%
3 5
 
0.1%
4 21
0.5%
5 6
 
0.1%
6 5
 
0.1%
7 12
0.3%
8 5
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
1890 1
< 0.1%
1887 1
< 0.1%
1885 1
< 0.1%
1883 1
< 0.1%
1882 1
< 0.1%
1881 1
< 0.1%
1879 1
< 0.1%
1874 2
< 0.1%
1869 1
< 0.1%
1862 1
< 0.1%

Cabin_side
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing100
Missing (%)2.3%
Memory size33.5 KiB
S
2093 
P
2084 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 2093
48.9%
P 2084
48.7%
(Missing) 100
 
2.3%

Length

2024-04-22T18:15:01.486144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T18:15:01.636235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
s 2093
50.1%
p 2084
49.9%

Most occurring characters

ValueCountFrequency (%)
S 2093
50.1%
P 2084
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2093
50.1%
P 2084
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2093
50.1%
P 2084
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2093
50.1%
P 2084
49.9%

Interactions

2024-04-22T18:14:06.156902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:08.717836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:18.112657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:29.209600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:38.461688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:47.626909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:57.178360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:14:11.162440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:08.934776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:18.309090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:29.373552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:38.620788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:47.828153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:57.326709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:14:15.684914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:09.150767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:18.458310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:29.530607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:38.833290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:47.998510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:57.484228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:14:20.846227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:09.339402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:18.615959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:29.702906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:38.966877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:48.176182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:57.628729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:14:25.386509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:09.579859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:18.798586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:29.848885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:39.125726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:48.333806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:57.790612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:14:30.143632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:09.818722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:19.014610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:30.033533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:39.331890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:48.491783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:57.943406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:14:35.394371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:10.032306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:19.192473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:30.220038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:39.547356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:48.679598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-22T18:13:58.121614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-22T18:14:52.265945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T18:14:52.637328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameCabin_deckCabin_numCabin_side
00013_01EarthTrueG/3/STRAPPIST-1e27.0False0.00.00.00.00.0Nelly CarsoningG3S
10018_01EarthFalseF/4/STRAPPIST-1e19.0False0.09.00.02823.00.0Lerome PeckersF4S
20019_01EuropaTrueC/0/S55 Cancri e31.0False0.00.00.00.00.0Sabih UnhearfusC0S
30021_01EuropaFalseC/1/STRAPPIST-1e38.0False0.06652.00.0181.0585.0Meratz CaltilterC1S
40023_01EarthFalseF/5/STRAPPIST-1e20.0False10.00.0635.00.00.0Brence HarperezF5S
50027_01EarthFalseF/7/PTRAPPIST-1e31.0False0.01615.0263.0113.060.0Karlen RicksF7P
60029_01EuropaTrueB/2/P55 Cancri e21.0False0.0NaN0.00.00.0Aldah AinserfleB2P
70032_01EuropaTrueD/0/STRAPPIST-1e20.0False0.00.00.00.00.0Acrabi PringryD0S
80032_02EuropaTrueD/0/S55 Cancri e23.0False0.00.00.00.00.0Dhena PringryD0S
90033_01EarthFalseF/7/S55 Cancri e24.0False0.0639.00.00.00.0Eliana DelazarsonF7S
PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameCabin_deckCabin_numCabin_side
42679260_01EarthTrueG/1503/P55 Cancri e3.0NaN0.00.00.00.00.0Luisy PortananneyG1503P
42689262_01EarthFalseF/1795/S55 Cancri e20.0False0.0601.0103.035.00.0Sonald HurchrisongF1795S
42699263_01EarthTrueG/1495/STRAPPIST-1e43.0False0.00.00.00.00.0Loisey HeneyG1495S
42709265_01MarsFalseD/278/STRAPPIST-1e43.0False47.00.03851.00.00.0Toate CureD278S
42719266_01EarthFalseF/1796/STRAPPIST-1e40.0False0.0865.00.03.00.0Danna PeterF1796S
42729266_02EarthTrueG/1496/STRAPPIST-1e34.0False0.00.00.00.00.0Jeron PeterG1496S
42739269_01EarthFalseNaNTRAPPIST-1e42.0False0.0847.017.010.0144.0Matty ScheronNaNNaNNaN
42749271_01MarsTrueD/296/P55 Cancri eNaNFalse0.00.00.00.00.0Jayrin PoreD296P
42759273_01EuropaFalseD/297/PNaNNaNFalse0.02680.00.00.0523.0Kitakan ConaleD297P
42769277_01EarthTrueG/1498/SPSO J318.5-2243.0False0.00.00.00.00.0Lilace LeonzaleyG1498S